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Frequency Detection of Weak Periodic Signal Based on Modified ADVP Model

机译:基于改进的ADVP模型的弱周期信号频率检测

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Detecting weak periodic signal by chaotic oscillator is not only a new method, but also practical applications on chaos theory. However, when using this method to detect weak periodic signal, we have to know the frequency of weak periodic signal in advance, which limits applications of the method. This paper studies the relation between the frequency of measured weak periodic signal and max-variance of output signal. Based on modified ADVP model, we search max-variance of output signal by genetic algorithm. With this new method, we obtain the frequency of measured weak periodic signal besides verify existence of weak periodic signal, which can remove the limitation of detecting weak periodic signal by chaotic oscillator. At the end, we successfully apply the new method to detect frequency of person's breath.
机译:利用混沌振荡器检测微弱的周期信号不仅是一种新方法,而且在混沌理论上也有实际应用。但是,当使用这种方法来检测微弱周期信号时,我们必须提前知道微弱周期信号的频率,这限制了该方法的应用。本文研究了测得的微弱周期信号的频率与输出信号的最大方差之间的关系。在改进的ADVP模型的基础上,采用遗传算法搜索输出信号的最大方差。利用该新方法,除了验证弱周期信号的存在性外,还可以获取被测弱周期信号的频率,从而消除了混沌振荡器检测弱周期信号的局限性。最后,我们成功地将新方法应用于检测人的呼吸频率。

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